{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 本周内容纲要\n",
    "<div class=\"bg-reshape\"></div>\n",
    "\n",
    "<div class=\"break\"></div>\n",
    "<div class=\"break\"></div>\n",
    "\n",
    "\n",
    "### 本周主要内容：\n",
    "\n",
    "以***独角兽数据***案例进行数据框数据重塑与数据透视表**系统说明和实践**：\n",
    "![](https://support.content.office.net/en-us/media/095beeee-8781-4b14-be42-9b05e9aff8d3.png#thumbnailauto)\n",
    "\n",
    "1. 数据重塑（Reshape）：\n",
    "  1. 对表格行进行排序（sort_values）\n",
    "  2. 长到宽表格式（pivot）\n",
    "2. 数据透视表（pivot, pivot_table）\n",
    "  1. 对表格行进行排序（sort_values）\n",
    "  2. 长到宽表格式（pivot）\n",
    "3. 比较数据透视表（pivot, pivot_table）\n",
    "  1. 和Excel相比\n",
    "  2. 和gropby相比\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "-----\n",
    "\n",
    "\n",
    "## 视觉化纲要\n",
    "\n",
    "-----\n",
    "\n",
    "### 知识点\n",
    "\n",
    "![](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/reshaping_pivot.png)\n",
    "本周知识点主要为**在二维项度**上，以\"类别\"分进，以\"数量\"合击，内容包括以下：\n",
    "\n",
    "* 数据科学之分进合击**进阶**分析策略\n",
    "  * 什麽是pivot, pivot_table, cross_tab数据透序表\n",
    "  * 这为什麽对\"出报表\"很重要？\n",
    "* **复习**分进合击操演的心法及剑法学习\n",
    "  * 区分变量性质/数据型态 (variable types / data types)  \n",
    "  * 分进多为\"类别\"，可以是定类或定序\n",
    "  * 合击常为\"数量\"，可以是定距或定比\n",
    "* 二维项度的设定决定及规划\n",
    "  * 使用nunique预判生成较宽表格的大小\n",
    "  * 多层次下的较复杂排序\n",
    "  * 数据框之\"长\"vs.\"宽\"的使用差异\n",
    "-----\n",
    "\n",
    "### 实践\n",
    "\n",
    "以数个\"类别\"分进，再以\"数量\"合击初步实践2019胡润全球独角兽榜数据。本周实践内容共分5段：\n",
    "\n",
    "1. 数据框出交叉报表\n",
    "2. 数据框排序（sorted_values）\n",
    "3. pivot说明\n",
    "4. pivot_table说明\n",
    "5. 数据感**进阶**：从聚合到解聚的操作感走向二维\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据框出交叉报表\n",
    "\n",
    "数据科学之分进合击进阶分析策略，在本周会靠数据重塑（Reshape）与数据透视表（pivot, pivot_table）更上一层楼\n",
    "\n",
    "* [參考1](https://pandas.pydata.org/pandas-docs/version/1.0.2/getting_started/intro_tutorials/07_reshape_table_layout.html#min-tut-07-reshape)\n",
    "* [參考2](https://pandas.pydata.org/pandas-docs/version/1.0.2/user_guide/reshaping.html#reshaping)\n",
    "\n",
    "\n",
    "## 初阶A-0：分进合击"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "import pandas as pd\n",
    "df = pd.read_csv('20春_pandas_week02_hurun_unicorn.tsv',encoding='utf-8',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       排名                   企业名称           Company Name  估值（亿人民币）  国家   城市  \\\n",
       "0      1                   蚂蚁金服          Ant Financial     10000  中国   杭州   \n",
       "1      2                   字节跳动              Bytedance      5000  中国   北京   \n",
       "2      3                   滴滴出行           Didi Chuxing      3600  中国   北京   \n",
       "3      4                  Infor                  Infor      3500  美国   纽约   \n",
       "4      5              JUUL Labs              JUUL Labs      3400  美国  旧金山   \n",
       "..   ...                    ...                    ...       ...  ..  ...   \n",
       "489  264            Zeta Global            Zeta Global        70  美国   纽约   \n",
       "490  264                  掌门1对1               Zhangmen        70  中国   上海   \n",
       "491  264                     转转             Zhuanzhuan        70  中国   北京   \n",
       "492  264  Zipline International  Zipline International        70  美国  半月湾   \n",
       "493  264           ZipRecruiter           ZipRecruiter        70  美国  洛杉矶   \n",
       "\n",
       "        行业                                            掌门人/创始人  成立年份  \\\n",
       "0     金融科技                                                井贤栋  2014   \n",
       "1    媒体和娱乐                                                张一鸣  2012   \n",
       "2     共享经济                                                 程维  2012   \n",
       "3      云计算                                        Jim Schaper  2002   \n",
       "4      消费品  Adam Bowen, James Monsees, Kevin Burns, Tim Da...  2015   \n",
       "..     ...                                                ...   ...   \n",
       "489   人工智能                   David A. Steinberg, John Sculley  2007   \n",
       "490   教育科技                                                 张翼  2014   \n",
       "491   电子商务                                                姚劲波  2015   \n",
       "492     物流       Keenan Wyrobek, Keller Rinaudo, Will Hetzler  2014   \n",
       "493   电子商务  Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd  2010   \n",
       "\n",
       "                                                部分投资机构  \n",
       "0                                       春华资本、中投海外、红杉资本  \n",
       "1                                  红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2                               腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  \n",
       "3         Golden Gate Capital, Koch Equity Development  \n",
       "4    M13, Timothy Davis, Evolution VC Partners, Tig...  \n",
       "..                                                 ...  \n",
       "489                  GPI Capital, GSO Capital Partners  \n",
       "490                                     顺为资本、达晨创投、华平投资  \n",
       "491                                                 腾讯  \n",
       "492  Sequoia Capital, Visionnaire Ventures, Katalys...  \n",
       "493               IVP (Institutional Venture Partners)  \n",
       "\n",
       "[494 rows x 10 columns]>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>成立年份</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">中国</th>\n",
       "      <th>2000</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>2005</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>2017</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          估值（亿人民币）\n",
       "国家  成立年份          \n",
       "中国  2000       170\n",
       "    2001       170\n",
       "    2002       200\n",
       "    2003       200\n",
       "    2004       100\n",
       "...            ...\n",
       "韩国  2005        70\n",
       "    2007       350\n",
       "    2010       670\n",
       "    2011       270\n",
       "马耳他 2017       150\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按国家+成立年份分组，用agg聚合分组后的估值数据\n",
    "分进合击初阶 = df.groupby(by =['国家','成立年份'])\\\n",
    "                .agg({\"估值（亿人民币）\":\"sum\"})\n",
    "分进合击初阶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意上表是有100列，1行数据\n",
    "\n",
    "若想要把成立年份变量往行变量放呢？\n",
    "\n",
    "## 迸阶A-1：分进合击pivot\n",
    "\n",
    "![](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/reshaping_pivot.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 20)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份     2009    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "国家                                                                        \n",
       "中国      950.0  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "以色列       NaN   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "卢森堡       NaN     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "印度        NaN  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "印度尼西亚   500.0   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "哥伦比亚      NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "巴西        NaN     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "德国       70.0     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "新加坡       NaN     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "日本        NaN     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "法国        NaN     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "澳大利亚      NaN     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "爱尔兰       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "爱沙尼亚      NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "瑞典        NaN     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "瑞士        NaN     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "美国     2090.0  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "芬兰        NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "英国       70.0     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "菲律宾       NaN     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "西班牙       NaN     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "阿根廷       NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "韩国        NaN   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "马耳他       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "\n",
       "成立年份    2017    2018   2019  \n",
       "国家                           \n",
       "中国     810.0  1090.0  100.0  \n",
       "以色列      NaN     NaN    NaN  \n",
       "卢森堡      NaN     NaN    NaN  \n",
       "印度      70.0     NaN    NaN  \n",
       "印度尼西亚    NaN     NaN    NaN  \n",
       "哥伦比亚     NaN     NaN    NaN  \n",
       "巴西       NaN     NaN    NaN  \n",
       "德国       NaN     NaN    NaN  \n",
       "新加坡      NaN     NaN    NaN  \n",
       "日本       NaN     NaN    NaN  \n",
       "法国       NaN     NaN    NaN  \n",
       "澳大利亚     NaN     NaN    NaN  \n",
       "爱尔兰      NaN     NaN    NaN  \n",
       "爱沙尼亚     NaN     NaN    NaN  \n",
       "瑞典       NaN     NaN    NaN  \n",
       "瑞士       NaN     NaN    NaN  \n",
       "美国     870.0     NaN   70.0  \n",
       "芬兰       NaN     NaN    NaN  \n",
       "英国       NaN     NaN    NaN  \n",
       "菲律宾      NaN     NaN    NaN  \n",
       "西班牙      NaN     NaN    NaN  \n",
       "阿根廷      NaN     NaN    NaN  \n",
       "韩国       NaN     NaN    NaN  \n",
       "马耳他    150.0     NaN    NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "分进合击进阶pv = df.groupby(by = ['国家','成立年份']) \\\n",
    "                   .agg({'估值（亿人民币）':'sum'}) \\\n",
    "# 重置索引，改为国家\n",
    "# pivot将列变成行\n",
    "# .shape查询数据表的形状，即几行几列\n",
    "                   .reset_index() \\\n",
    "                   .set_index(['国家']) \\\n",
    "                   .pivot(columns = '成立年份',values = '估值（亿人民币）')\n",
    "                   \n",
    "print(分进合击进阶pv.shape)          \n",
    "分进合击进阶pv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意上表是有24行20列数据，\n",
    "\n",
    "成功的成立年份变量往行变量放，不过因有缺数据，20*24 > 100\n",
    "\n",
    "## 迸阶A-2：分进合击pivot_table\n",
    "- [pivot_table参数解释博客](https://www.cnblogs.com/Yanjy-OnlyOne/p/11195621.html)\n",
    "- [Pandas_聚合数据_pivot_table()博客参考](https://blog.csdn.net/mingkoukou/article/details/82870960)\n",
    "- pivot() 只能将列数据转换成行索引和列索引，不能运算，而且如果某项数据出现重复时，将无法执行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 20)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份     2009    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "国家                                                                        \n",
       "中国      950.0  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "以色列       NaN   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "卢森堡       NaN     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "印度        NaN  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "印度尼西亚   500.0   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "哥伦比亚      NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "巴西        NaN     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "德国       70.0     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "新加坡       NaN     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "日本        NaN     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "法国        NaN     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "澳大利亚      NaN     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "爱尔兰       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "爱沙尼亚      NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "瑞典        NaN     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "瑞士        NaN     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "美国     2090.0  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "芬兰        NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "英国       70.0     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "菲律宾       NaN     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "西班牙       NaN     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "阿根廷       NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "韩国        NaN   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "马耳他       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "\n",
       "成立年份    2017    2018   2019  \n",
       "国家                           \n",
       "中国     810.0  1090.0  100.0  \n",
       "以色列      NaN     NaN    NaN  \n",
       "卢森堡      NaN     NaN    NaN  \n",
       "印度      70.0     NaN    NaN  \n",
       "印度尼西亚    NaN     NaN    NaN  \n",
       "哥伦比亚     NaN     NaN    NaN  \n",
       "巴西       NaN     NaN    NaN  \n",
       "德国       NaN     NaN    NaN  \n",
       "新加坡      NaN     NaN    NaN  \n",
       "日本       NaN     NaN    NaN  \n",
       "法国       NaN     NaN    NaN  \n",
       "澳大利亚     NaN     NaN    NaN  \n",
       "爱尔兰      NaN     NaN    NaN  \n",
       "爱沙尼亚     NaN     NaN    NaN  \n",
       "瑞典       NaN     NaN    NaN  \n",
       "瑞士       NaN     NaN    NaN  \n",
       "美国     870.0     NaN   70.0  \n",
       "芬兰       NaN     NaN    NaN  \n",
       "英国       NaN     NaN    NaN  \n",
       "菲律宾      NaN     NaN    NaN  \n",
       "西班牙      NaN     NaN    NaN  \n",
       "阿根廷      NaN     NaN    NaN  \n",
       "韩国       NaN     NaN    NaN  \n",
       "马耳他    150.0     NaN    NaN  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-3 採用分进合击初阶分析策略  pivot_table\n",
    "# columns列 index行，索引\n",
    "分进合击迸阶 = df.pivot_table(index= \"国家\", columns=\"成立年份\",  \\\n",
    "                 values=\"估值（亿人民币）\", aggfunc=\"sum\") \n",
    "print (分进合击迸阶.shape)\n",
    "分进合击迸阶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['中国', '美国', '印度', '英国', '印度尼西亚', '韩国', '新加坡', '德国', '以色列', '瑞士', '巴西',\n",
       "       '瑞典', '法国', '日本', '澳大利亚', '爱尔兰', '马耳他', '爱沙尼亚', '芬兰', '哥伦比亚', '菲律宾',\n",
       "       '西班牙', '卢森堡', '阿根廷'],\n",
       "      dtype='object', name='国家')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-3 pivot_table 按总值排序\n",
    "# sum(axis=1)跨列求总值\n",
    "# sort_values排序，ascending=False降序\n",
    "# .index表变列表,按索引排序\n",
    "排序 = 分进合击迸阶.sum(axis=1).sort_values(ascending=False).index\n",
    "排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([54700., 47730.,  3850.,  2420.,  1570.,  1360.,  1350.,  1010.,\n",
       "         730.,   720.,   510.,   450.,   360.,   220.,   200.,   150.,\n",
       "         150.,    70.,    70.,    70.,    70.,    70.,    70.,    70.])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .values 按值排序\n",
    "排序_总值 = 分进合击迸阶.sum(axis=1).sort_values(ascending=False).values\n",
    "排序_总值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份     2009    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "国家                                                                        \n",
       "中国      950.0  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "美国     2090.0  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "印度        NaN  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "英国       70.0     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "印度尼西亚   500.0   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "韩国        NaN   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "新加坡       NaN     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "德国       70.0     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "以色列       NaN   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "瑞士        NaN     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "巴西        NaN     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "瑞典        NaN     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "法国        NaN     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "日本        NaN     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "澳大利亚      NaN     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "爱尔兰       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "马耳他       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "爱沙尼亚      NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "芬兰        NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "哥伦比亚      NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "菲律宾       NaN     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "西班牙       NaN     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "卢森堡       NaN     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "阿根廷       NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "\n",
       "成立年份    2017    2018   2019  \n",
       "国家                           \n",
       "中国     810.0  1090.0  100.0  \n",
       "美国     870.0     NaN   70.0  \n",
       "印度      70.0     NaN    NaN  \n",
       "英国       NaN     NaN    NaN  \n",
       "印度尼西亚    NaN     NaN    NaN  \n",
       "韩国       NaN     NaN    NaN  \n",
       "新加坡      NaN     NaN    NaN  \n",
       "德国       NaN     NaN    NaN  \n",
       "以色列      NaN     NaN    NaN  \n",
       "瑞士       NaN     NaN    NaN  \n",
       "巴西       NaN     NaN    NaN  \n",
       "瑞典       NaN     NaN    NaN  \n",
       "法国       NaN     NaN    NaN  \n",
       "日本       NaN     NaN    NaN  \n",
       "澳大利亚     NaN     NaN    NaN  \n",
       "爱尔兰      NaN     NaN    NaN  \n",
       "马耳他    150.0     NaN    NaN  \n",
       "爱沙尼亚     NaN     NaN    NaN  \n",
       "芬兰       NaN     NaN    NaN  \n",
       "哥伦比亚     NaN     NaN    NaN  \n",
       "菲律宾      NaN     NaN    NaN  \n",
       "西班牙      NaN     NaN    NaN  \n",
       "卢森堡      NaN     NaN    NaN  \n",
       "阿根廷      NaN     NaN    NaN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .loc[排序]按索引排序排列\n",
    "分进合击迸阶_排序过 =  分进合击迸阶.loc[排序]\n",
    "分进合击迸阶_排序过"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>...</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>总值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>54700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>47730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份     2009  ...    2011     2012    2013     2014    2015    2016   2017  \\\n",
       "国家             ...                                                            \n",
       "中国      950.0  ...  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0  810.0   \n",
       "美国     2090.0  ...  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0  870.0   \n",
       "印度        NaN  ...   440.0    140.0   350.0    270.0     NaN    70.0   70.0   \n",
       "英国       70.0  ...   550.0    450.0   350.0      NaN   350.0   150.0    NaN   \n",
       "印度尼西亚   500.0  ...    70.0    300.0     NaN      NaN     NaN     NaN    NaN   \n",
       "韩国        NaN  ...   270.0      NaN     NaN      NaN     NaN     NaN    NaN   \n",
       "新加坡       NaN  ...     NaN   1350.0     NaN      NaN     NaN     NaN    NaN   \n",
       "德国       70.0  ...   150.0    370.0   200.0     70.0     NaN     NaN    NaN   \n",
       "以色列       NaN  ...   150.0    150.0    70.0      NaN     NaN     NaN    NaN   \n",
       "瑞士        NaN  ...     NaN     70.0     NaN    500.0   150.0     NaN    NaN   \n",
       "巴西        NaN  ...    70.0     70.0   370.0      NaN     NaN     NaN    NaN   \n",
       "瑞典        NaN  ...     NaN      NaN     NaN      NaN     NaN   150.0    NaN   \n",
       "法国        NaN  ...     NaN      NaN    70.0      NaN     NaN    70.0    NaN   \n",
       "日本        NaN  ...     NaN      NaN     NaN    220.0     NaN     NaN    NaN   \n",
       "澳大利亚      NaN  ...     NaN    200.0     NaN      NaN     NaN     NaN    NaN   \n",
       "爱尔兰       NaN  ...     NaN      NaN     NaN      NaN     NaN     NaN    NaN   \n",
       "马耳他       NaN  ...     NaN      NaN     NaN      NaN     NaN     NaN  150.0   \n",
       "爱沙尼亚      NaN  ...     NaN      NaN    70.0      NaN     NaN     NaN    NaN   \n",
       "芬兰        NaN  ...     NaN      NaN     NaN      NaN     NaN    70.0    NaN   \n",
       "哥伦比亚      NaN  ...     NaN      NaN     NaN      NaN     NaN    70.0    NaN   \n",
       "菲律宾       NaN  ...     NaN      NaN     NaN      NaN    70.0     NaN    NaN   \n",
       "西班牙       NaN  ...    70.0      NaN     NaN      NaN     NaN     NaN    NaN   \n",
       "卢森堡       NaN  ...     NaN      NaN     NaN     70.0     NaN     NaN    NaN   \n",
       "阿根廷       NaN  ...     NaN      NaN    70.0      NaN     NaN     NaN    NaN   \n",
       "\n",
       "成立年份     2018   2019       总值  \n",
       "国家                             \n",
       "中国     1090.0  100.0  54700.0  \n",
       "美国        NaN   70.0  47730.0  \n",
       "印度        NaN    NaN   3850.0  \n",
       "英国        NaN    NaN   2420.0  \n",
       "印度尼西亚     NaN    NaN   1570.0  \n",
       "韩国        NaN    NaN   1360.0  \n",
       "新加坡       NaN    NaN   1350.0  \n",
       "德国        NaN    NaN   1010.0  \n",
       "以色列       NaN    NaN    730.0  \n",
       "瑞士        NaN    NaN    720.0  \n",
       "巴西        NaN    NaN    510.0  \n",
       "瑞典        NaN    NaN    450.0  \n",
       "法国        NaN    NaN    360.0  \n",
       "日本        NaN    NaN    220.0  \n",
       "澳大利亚      NaN    NaN    200.0  \n",
       "爱尔兰       NaN    NaN    150.0  \n",
       "马耳他       NaN    NaN    150.0  \n",
       "爱沙尼亚      NaN    NaN     70.0  \n",
       "芬兰        NaN    NaN     70.0  \n",
       "哥伦比亚      NaN    NaN     70.0  \n",
       "菲律宾       NaN    NaN     70.0  \n",
       "西班牙       NaN    NaN     70.0  \n",
       "卢森堡       NaN    NaN     70.0  \n",
       "阿根廷       NaN    NaN     70.0  \n",
       "\n",
       "[24 rows x 21 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按值排序\n",
    "分进合击迸阶_排序过['总值'] = 排序_总值\n",
    "分进合击迸阶_排序过"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据框排序（sorted_values）\n",
    "交叉报表的排序较为复杂，有两维可以排序\n",
    "\n",
    "## 排序B-1 sort_index axis=0\n",
    "\n",
    "![](https://upload-images.jianshu.io/upload_images/2233157-b77105789e36c847.jpg)\n",
    "\n",
    "\n",
    "Series.sort_values（self，axis = 0，ascending = True，inplace = False，kind =' quicksort '，na_position ='last'，ignore_index = False ）\n",
    "* Series.sort_index (By index)   \n",
    "按系列索引排序。  \n",
    "\n",
    "* DataFrame.sort_values  (By values)  \n",
    "通过沿任一轴的值对DataFrame进行排序。  \n",
    "\n",
    "* DataFrame.sort_index  (By index)  \n",
    "按索引对DataFrame进行排序。  \n",
    "\n",
    "* By indexes and values\n",
    "\n",
    "更多排序内容请参见[basics-sorting](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-sorting)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [参考_Python之NumPy（axis=0 与axis=1）区分](https://www.cnblogs.com/rrttp/p/8028421.html)\n",
    "- 使用0值表示沿着每一列或行标签\\索引值向下执行方法\n",
    "- 使用1值表示沿着每一行或者列标签模向执行对应的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "\n",
       "成立年份     2009    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "国家                                                                        \n",
       "马耳他       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "韩国        NaN   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "阿根廷       NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "西班牙       NaN     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "菲律宾       NaN     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "英国       70.0     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "芬兰        NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "美国     2090.0  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "瑞士        NaN     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "瑞典        NaN     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "爱沙尼亚      NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "爱尔兰       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "澳大利亚      NaN     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "法国        NaN     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "日本        NaN     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "新加坡       NaN     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "德国       70.0     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "巴西        NaN     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "哥伦比亚      NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "印度尼西亚   500.0   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "印度        NaN  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "卢森堡       NaN     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "以色列       NaN   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "中国      950.0  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "\n",
       "成立年份    2017    2018   2019  \n",
       "国家                           \n",
       "马耳他    150.0     NaN    NaN  \n",
       "韩国       NaN     NaN    NaN  \n",
       "阿根廷      NaN     NaN    NaN  \n",
       "西班牙      NaN     NaN    NaN  \n",
       "菲律宾      NaN     NaN    NaN  \n",
       "英国       NaN     NaN    NaN  \n",
       "芬兰       NaN     NaN    NaN  \n",
       "美国     870.0     NaN   70.0  \n",
       "瑞士       NaN     NaN    NaN  \n",
       "瑞典       NaN     NaN    NaN  \n",
       "爱沙尼亚     NaN     NaN    NaN  \n",
       "爱尔兰      NaN     NaN    NaN  \n",
       "澳大利亚     NaN     NaN    NaN  \n",
       "法国       NaN     NaN    NaN  \n",
       "日本       NaN     NaN    NaN  \n",
       "新加坡      NaN     NaN    NaN  \n",
       "德国       NaN     NaN    NaN  \n",
       "巴西       NaN     NaN    NaN  \n",
       "哥伦比亚     NaN     NaN    NaN  \n",
       "印度尼西亚    NaN     NaN    NaN  \n",
       "印度      70.0     NaN    NaN  \n",
       "卢森堡      NaN     NaN    NaN  \n",
       "以色列      NaN     NaN    NaN  \n",
       "中国     810.0  1090.0  100.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B-1\n",
    "# axis=0代表往跨行（down)，而axis=1代表跨列（across）\n",
    "# 按列索引，跨行降序排序\n",
    "分进合击迸阶.sort_index(axis=0, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "\n",
       "成立年份     2009    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "国家                                                                        \n",
       "马耳他       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "韩国        NaN   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "阿根廷       NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "西班牙       NaN     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "菲律宾       NaN     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "英国       70.0     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "芬兰        NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "美国     2090.0  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "瑞士        NaN     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "瑞典        NaN     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "爱沙尼亚      NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "爱尔兰       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "澳大利亚      NaN     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "法国        NaN     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "日本        NaN     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "新加坡       NaN     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "德国       70.0     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "巴西        NaN     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "哥伦比亚      NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "印度尼西亚   500.0   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "印度        NaN  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "卢森堡       NaN     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "以色列       NaN   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "中国      950.0  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "\n",
       "成立年份    2017    2018   2019  \n",
       "国家                           \n",
       "马耳他    150.0     NaN    NaN  \n",
       "韩国       NaN     NaN    NaN  \n",
       "阿根廷      NaN     NaN    NaN  \n",
       "西班牙      NaN     NaN    NaN  \n",
       "菲律宾      NaN     NaN    NaN  \n",
       "英国       NaN     NaN    NaN  \n",
       "芬兰       NaN     NaN    NaN  \n",
       "美国     870.0     NaN   70.0  \n",
       "瑞士       NaN     NaN    NaN  \n",
       "瑞典       NaN     NaN    NaN  \n",
       "爱沙尼亚     NaN     NaN    NaN  \n",
       "爱尔兰      NaN     NaN    NaN  \n",
       "澳大利亚     NaN     NaN    NaN  \n",
       "法国       NaN     NaN    NaN  \n",
       "日本       NaN     NaN    NaN  \n",
       "新加坡      NaN     NaN    NaN  \n",
       "德国       NaN     NaN    NaN  \n",
       "巴西       NaN     NaN    NaN  \n",
       "哥伦比亚     NaN     NaN    NaN  \n",
       "印度尼西亚    NaN     NaN    NaN  \n",
       "印度      70.0     NaN    NaN  \n",
       "卢森堡      NaN     NaN    NaN  \n",
       "以色列      NaN     NaN    NaN  \n",
       "中国     810.0  1090.0  100.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B-1\n",
    "# axis=0代表往跨行（down)，而axis=1代表跨列（across）\n",
    "# 按列索引，跨列降序排序\n",
    "分进合击迸阶.sort_index(axis=0, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['中国', '美国', '印度', '英国', '印度尼西亚', '韩国', '新加坡', '德国', '以色列', '瑞士', '巴西',\n",
       "       '瑞典', '法国', '日本', '澳大利亚', '爱尔兰', '马耳他', '爱沙尼亚', '芬兰', '哥伦比亚', '菲律宾',\n",
       "       '西班牙', '卢森堡', '阿根廷'],\n",
       "      dtype='object', name='国家')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B-3 同 # A-3，一步步观察并学习\n",
    "# 将index以跨列降序的方式输出\n",
    "排序 = 分进合击迸阶.sum(axis=1).sort_values(ascending=False).index\n",
    "排序\n",
    "#排序_总值 = 分进合击迸阶.sum(axis=1).sort_values(ascending=False).values\n",
    "#分进合击迸阶_排序过 =  分进合击迸阶.loc[排序]\n",
    "#分进合击迸阶_排序过['总值'] = 排序_总值\n",
    "#分进合击迸阶_排序过"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  pivot说明\n",
    "\n",
    "## pivot 长框变宽框\n",
    "\n",
    "![](https://pandas.pydata.org/pandas-docs/stable/_images/07_pivot.svg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分+进\n",
    "\n",
    "1. groupby 分组  \n",
    "2. agg 计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>成立年份</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">中国</th>\n",
       "      <th>2000</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>2005</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>2017</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          估值（亿人民币）\n",
       "国家  成立年份          \n",
       "中国  2000       170\n",
       "    2001       170\n",
       "    2002       200\n",
       "    2003       200\n",
       "    2004       100\n",
       "...            ...\n",
       "韩国  2005        70\n",
       "    2007       350\n",
       "    2010       670\n",
       "    2011       270\n",
       "马耳他 2017       150\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C-1 迸阶A-1分解动作 groupby\n",
    "分进合击初阶 = df.groupby(by= [\"国家\", \"成立年份\"]) \\\n",
    "                .agg({\"估值（亿人民币）\":\"sum\"})\n",
    "分进合击初阶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### reset_index\n",
    "\n",
    "* #D-1 reset_index 进行默认索引，取消groupby分组,将组别变成普通的列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>国家</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中国</td>\n",
       "      <td>2000</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>中国</td>\n",
       "      <td>2001</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中国</td>\n",
       "      <td>2002</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>中国</td>\n",
       "      <td>2003</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中国</td>\n",
       "      <td>2004</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>韩国</td>\n",
       "      <td>2005</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>韩国</td>\n",
       "      <td>2007</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>韩国</td>\n",
       "      <td>2010</td>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>韩国</td>\n",
       "      <td>2011</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>马耳他</td>\n",
       "      <td>2017</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     国家  成立年份  估值（亿人民币）\n",
       "0    中国  2000       170\n",
       "1    中国  2001       170\n",
       "2    中国  2002       200\n",
       "3    中国  2003       200\n",
       "4    中国  2004       100\n",
       "..  ...   ...       ...\n",
       "95   韩国  2005        70\n",
       "96   韩国  2007       350\n",
       "97   韩国  2010       670\n",
       "98   韩国  2011       270\n",
       "99  马耳他  2017       150\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C-2 迸阶A-2分解动作  level 如果分组在两层或者以上，level(按列计算，分组从最高级往下分别为[0,1,2,3,4])\n",
    "分进合击初阶.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pivot 指定列index、行columns、值values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>国家</th>\n",
       "      <th>中国</th>\n",
       "      <th>以色列</th>\n",
       "      <th>卢森堡</th>\n",
       "      <th>印度</th>\n",
       "      <th>印度尼西亚</th>\n",
       "      <th>哥伦比亚</th>\n",
       "      <th>巴西</th>\n",
       "      <th>德国</th>\n",
       "      <th>新加坡</th>\n",
       "      <th>日本</th>\n",
       "      <th>...</th>\n",
       "      <th>瑞典</th>\n",
       "      <th>瑞士</th>\n",
       "      <th>美国</th>\n",
       "      <th>芬兰</th>\n",
       "      <th>英国</th>\n",
       "      <th>菲律宾</th>\n",
       "      <th>西班牙</th>\n",
       "      <th>阿根廷</th>\n",
       "      <th>韩国</th>\n",
       "      <th>马耳他</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>成立年份</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>200.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>420.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>810.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1280.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>710.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>950.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1590.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>6570.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>440.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>11330.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>5340.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>16150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>3960.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>810.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1090.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "国家         中国    以色列   卢森堡      印度  印度尼西亚  哥伦比亚     巴西     德国     新加坡     日本  \\\n",
       "成立年份                                                                           \n",
       "2000    170.0    NaN   NaN   150.0    NaN   NaN    NaN  150.0     NaN    NaN   \n",
       "2001    170.0    NaN   NaN     NaN    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "2002    200.0  150.0   NaN     NaN    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "2003    200.0    NaN   NaN     NaN    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "2004    100.0    NaN   NaN   150.0    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "2005    300.0    NaN   NaN     NaN    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "2006   2380.0    NaN   NaN     NaN    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "2007   1280.0    NaN   NaN    70.0    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "2008    710.0    NaN   NaN   840.0    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "2009    950.0    NaN   NaN     NaN  500.0   NaN    NaN   70.0     NaN    NaN   \n",
       "2010   1590.0  210.0   NaN  1300.0  700.0   NaN    NaN    NaN     NaN    NaN   \n",
       "2011   6570.0  150.0   NaN   440.0   70.0   NaN   70.0  150.0     NaN    NaN   \n",
       "2012  11330.0  150.0   NaN   140.0  300.0   NaN   70.0  370.0  1350.0    NaN   \n",
       "2013   5340.0   70.0   NaN   350.0    NaN   NaN  370.0  200.0     NaN    NaN   \n",
       "2014  16150.0    NaN  70.0   270.0    NaN   NaN    NaN   70.0     NaN  220.0   \n",
       "2015   3960.0    NaN   NaN     NaN    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "2016   1300.0    NaN   NaN    70.0    NaN  70.0    NaN    NaN     NaN    NaN   \n",
       "2017    810.0    NaN   NaN    70.0    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "2018   1090.0    NaN   NaN     NaN    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "2019    100.0    NaN   NaN     NaN    NaN   NaN    NaN    NaN     NaN    NaN   \n",
       "\n",
       "国家    ...     瑞典     瑞士      美国    芬兰     英国   菲律宾   西班牙   阿根廷     韩国    马耳他  \n",
       "成立年份  ...                                                                     \n",
       "2000  ...    NaN    NaN   210.0   NaN    NaN   NaN   NaN   NaN    NaN    NaN  \n",
       "2001  ...    NaN    NaN   210.0   NaN    NaN   NaN   NaN   NaN    NaN    NaN  \n",
       "2002  ...    NaN    NaN  6270.0   NaN    NaN   NaN   NaN   NaN    NaN    NaN  \n",
       "2003  ...    NaN    NaN   420.0   NaN    NaN   NaN   NaN   NaN    NaN    NaN  \n",
       "2004  ...    NaN    NaN  1550.0   NaN  350.0   NaN   NaN   NaN    NaN    NaN  \n",
       "2005  ...  300.0    NaN   840.0   NaN  150.0   NaN   NaN   NaN   70.0    NaN  \n",
       "2006  ...    NaN    NaN   810.0   NaN    NaN   NaN   NaN   NaN    NaN    NaN  \n",
       "2007  ...    NaN    NaN  2650.0   NaN    NaN   NaN   NaN   NaN  350.0    NaN  \n",
       "2008  ...    NaN    NaN  4570.0   NaN    NaN   NaN   NaN   NaN    NaN    NaN  \n",
       "2009  ...    NaN    NaN  2090.0   NaN   70.0   NaN   NaN   NaN    NaN    NaN  \n",
       "2010  ...    NaN    NaN  6150.0   NaN    NaN   NaN   NaN   NaN  670.0    NaN  \n",
       "2011  ...    NaN    NaN  2620.0   NaN  550.0   NaN  70.0   NaN  270.0    NaN  \n",
       "2012  ...    NaN   70.0  4670.0   NaN  450.0   NaN   NaN   NaN    NaN    NaN  \n",
       "2013  ...    NaN    NaN  3840.0   NaN  350.0   NaN   NaN  70.0    NaN    NaN  \n",
       "2014  ...    NaN  500.0  2220.0   NaN    NaN   NaN   NaN   NaN    NaN    NaN  \n",
       "2015  ...    NaN  150.0  5980.0   NaN  350.0  70.0   NaN   NaN    NaN    NaN  \n",
       "2016  ...  150.0    NaN  1690.0  70.0  150.0   NaN   NaN   NaN    NaN    NaN  \n",
       "2017  ...    NaN    NaN   870.0   NaN    NaN   NaN   NaN   NaN    NaN  150.0  \n",
       "2018  ...    NaN    NaN     NaN   NaN    NaN   NaN   NaN   NaN    NaN    NaN  \n",
       "2019  ...    NaN    NaN    70.0   NaN    NaN   NaN   NaN   NaN    NaN    NaN  \n",
       "\n",
       "[20 rows x 24 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C-3 迸阶A-2分解动作\n",
    "分进合击初阶_pivot = 分进合击初阶.reset_index().pivot(index = '成立年份',columns='国家' ,values='估值（亿人民币）' )\n",
    "分进合击初阶_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20f7a82ff88>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 31435 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20221 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 31435 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20221 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23478 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20197 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 33394 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21015 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21346 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 26862 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22561 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21360 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24230 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23612 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 35199 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21733 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20262 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24052 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24503 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 26032 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21152 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22369 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 26412 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 27861 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 28595 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22823 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21033 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 29233 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23572 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20848 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 27801 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 29790 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20856 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22763 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32654 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 33452 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 33521 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 33778 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24459 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23486 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 29677 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 29273 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 38463 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 26681 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24311 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 38889 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 39532 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32819 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20182 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23478 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20197 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 33394 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21015 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21346 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 26862 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22561 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21360 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24230 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23612 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 35199 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21733 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20262 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24052 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24503 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 26032 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21152 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22369 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 26412 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 27861 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 28595 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22823 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21033 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 29233 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23572 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20848 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 27801 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 29790 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20856 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22763 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32654 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 33452 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 33521 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 33778 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24459 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23486 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 29677 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 29273 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 38463 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 26681 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24311 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 38889 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 39532 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32819 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20182 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# C-4 plot 绘制图表\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['Songti SC'] # 设置参数字体\n",
    "分进合击初阶_pivot.plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [matplotlib参考](https://www.jianshu.com/p/da385a35f68d)\n",
    "- 利用函数的调用，MATLAB中可以轻松的利用一行命令来绘制直线，然后再用一系列的函数调整结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://pandas.pydata.org/pandas-docs/stable/_images/07_pivot_table.svg)\n",
    "\n",
    "#  pivot_table说明\n",
    "\n",
    "* [什么是透视表？](https://zhuanlan.zhihu.com/p/31952948)\n",
    "\n",
    "\n",
    "## pivot_table 长框变宽框\n",
    "pivot_table 分解动作及作图\n",
    "\n",
    "### pivot_table\n",
    "* pivot_table 比pivot 多function运算\n",
    "* 这样相当于groupby+agg+pivot 的结果可用更好的pivot_table\n",
    "\n",
    "上面两代碼等价，pivot_table仿佛是加入了columns与margin功能的groupby函数，比groupby更加灵活。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "-----\n",
    "\n",
    "\n",
    "## 以下小心使用, 不然就刪\n",
    "\n",
    "\n",
    "\n",
    "-----\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>成立年份</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">中国</th>\n",
       "      <th>2000</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>2005</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>2017</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          估值（亿人民币）\n",
       "国家  成立年份          \n",
       "中国  2000       170\n",
       "    2001       170\n",
       "    2002       200\n",
       "    2003       200\n",
       "    2004       100\n",
       "...            ...\n",
       "韩国  2005        70\n",
       "    2007       350\n",
       "    2010       670\n",
       "    2011       270\n",
       "马耳他 2017       150\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# D-0\n",
    "df.groupby([\"国家\",\"成立年份\"]).agg({\"估值（亿人民币）\":sum}).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>成立年份</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">中国</th>\n",
       "      <th>2000</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">韩国</th>\n",
       "      <th>2005</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>2017</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          估值（亿人民币）\n",
       "国家  成立年份          \n",
       "中国  2000       170\n",
       "    2001       170\n",
       "    2002       200\n",
       "    2003       200\n",
       "    2004       100\n",
       "...            ...\n",
       "韩国  2005        70\n",
       "    2007       350\n",
       "    2010       670\n",
       "    2011       270\n",
       "马耳他 2017       150\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# D-1\n",
    "df.groupby([\"国家\",\"成立年份\"])[[\"估值（亿人民币）\"]].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2005</th>\n",
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       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
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       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
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       "    <tr>\n",
       "      <th>国家</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>6570.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哥伦比亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴西</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份    2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "国家                                                                          \n",
       "中国     170.0  170.0   200.0  200.0   100.0  300.0  2380.0  1280.0   710.0   \n",
       "以色列      NaN    NaN   150.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "卢森堡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "印度     150.0    NaN     NaN    NaN   150.0    NaN     NaN    70.0   840.0   \n",
       "印度尼西亚    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "哥伦比亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "巴西       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "德国     150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新加坡      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "日本       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "法国       NaN    NaN     NaN    NaN     NaN    NaN   220.0     NaN     NaN   \n",
       "澳大利亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱尔兰    150.0    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "爱沙尼亚     NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "瑞典       NaN    NaN     NaN    NaN     NaN  300.0     NaN     NaN     NaN   \n",
       "瑞士       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "美国     210.0  210.0  6270.0  420.0  1550.0  840.0   810.0  2650.0  4570.0   \n",
       "芬兰       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "英国       NaN    NaN     NaN    NaN   350.0  150.0     NaN     NaN     NaN   \n",
       "菲律宾      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "西班牙      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "阿根廷      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "韩国       NaN    NaN     NaN    NaN     NaN   70.0     NaN   350.0     NaN   \n",
       "马耳他      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "成立年份     2009    2010    2011     2012    2013     2014    2015    2016  \\\n",
       "国家                                                                        \n",
       "中国      950.0  1590.0  6570.0  11330.0  5340.0  16150.0  3960.0  1300.0   \n",
       "以色列       NaN   210.0   150.0    150.0    70.0      NaN     NaN     NaN   \n",
       "卢森堡       NaN     NaN     NaN      NaN     NaN     70.0     NaN     NaN   \n",
       "印度        NaN  1300.0   440.0    140.0   350.0    270.0     NaN    70.0   \n",
       "印度尼西亚   500.0   700.0    70.0    300.0     NaN      NaN     NaN     NaN   \n",
       "哥伦比亚      NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "巴西        NaN     NaN    70.0     70.0   370.0      NaN     NaN     NaN   \n",
       "德国       70.0     NaN   150.0    370.0   200.0     70.0     NaN     NaN   \n",
       "新加坡       NaN     NaN     NaN   1350.0     NaN      NaN     NaN     NaN   \n",
       "日本        NaN     NaN     NaN      NaN     NaN    220.0     NaN     NaN   \n",
       "法国        NaN     NaN     NaN      NaN    70.0      NaN     NaN    70.0   \n",
       "澳大利亚      NaN     NaN     NaN    200.0     NaN      NaN     NaN     NaN   \n",
       "爱尔兰       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "爱沙尼亚      NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "瑞典        NaN     NaN     NaN      NaN     NaN      NaN     NaN   150.0   \n",
       "瑞士        NaN     NaN     NaN     70.0     NaN    500.0   150.0     NaN   \n",
       "美国     2090.0  6150.0  2620.0   4670.0  3840.0   2220.0  5980.0  1690.0   \n",
       "芬兰        NaN     NaN     NaN      NaN     NaN      NaN     NaN    70.0   \n",
       "英国       70.0     NaN   550.0    450.0   350.0      NaN   350.0   150.0   \n",
       "菲律宾       NaN     NaN     NaN      NaN     NaN      NaN    70.0     NaN   \n",
       "西班牙       NaN     NaN    70.0      NaN     NaN      NaN     NaN     NaN   \n",
       "阿根廷       NaN     NaN     NaN      NaN    70.0      NaN     NaN     NaN   \n",
       "韩国        NaN   670.0   270.0      NaN     NaN      NaN     NaN     NaN   \n",
       "马耳他       NaN     NaN     NaN      NaN     NaN      NaN     NaN     NaN   \n",
       "\n",
       "成立年份    2017    2018   2019  \n",
       "国家                           \n",
       "中国     810.0  1090.0  100.0  \n",
       "以色列      NaN     NaN    NaN  \n",
       "卢森堡      NaN     NaN    NaN  \n",
       "印度      70.0     NaN    NaN  \n",
       "印度尼西亚    NaN     NaN    NaN  \n",
       "哥伦比亚     NaN     NaN    NaN  \n",
       "巴西       NaN     NaN    NaN  \n",
       "德国       NaN     NaN    NaN  \n",
       "新加坡      NaN     NaN    NaN  \n",
       "日本       NaN     NaN    NaN  \n",
       "法国       NaN     NaN    NaN  \n",
       "澳大利亚     NaN     NaN    NaN  \n",
       "爱尔兰      NaN     NaN    NaN  \n",
       "爱沙尼亚     NaN     NaN    NaN  \n",
       "瑞典       NaN     NaN    NaN  \n",
       "瑞士       NaN     NaN    NaN  \n",
       "美国     870.0     NaN   70.0  \n",
       "芬兰       NaN     NaN    NaN  \n",
       "英国       NaN     NaN    NaN  \n",
       "菲律宾      NaN     NaN    NaN  \n",
       "西班牙      NaN     NaN    NaN  \n",
       "阿根廷      NaN     NaN    NaN  \n",
       "韩国       NaN     NaN    NaN  \n",
       "马耳他    150.0     NaN    NaN  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# D-2\n",
    "分进合击初阶_pivot_table = 分进合击初阶.reset_index().pivot_table(index = '国家' ,columns='成立年份',values='估值（亿人民币）',aggfunc=\"sum\")\n",
    "分进合击初阶_pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available matplotlib backends: ['tk', 'gtk', 'gtk3', 'wx', 'qt4', 'qt5', 'qt', 'osx', 'nbagg', 'notebook', 'agg', 'svg', 'pdf', 'ps', 'inline', 'ipympl', 'widget']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20f7b37a508>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21733 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20262 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 27861 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 29790 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22763 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 35199 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 29677 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 29273 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21733 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20262 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 27861 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 29790 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22763 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 35199 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 29677 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 29273 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23478 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23478 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 31435 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20221 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 31435 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20221 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib --list\n",
    "# D-3\n",
    "分进合击初阶_pivot_table.plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 练习：按照年份，将不同行业的估值趋势进行描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv (\"20春_pandas_week02_hurun_unicorn.tsv\", encoding = \"utf8\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实践pivot_table方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3D印刷</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <td>290.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>290.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>即时通讯</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>590.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新零售</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生命科学</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>网络安全</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>570.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>航天</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>虚拟与增强现实</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件与服务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>4040.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>11570.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份      2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "行业                                                                            \n",
       "3D印刷       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "云计算      290.0   70.0  3500.0  150.0     NaN   70.0     NaN     NaN   290.0   \n",
       "人工智能       NaN    NaN     NaN  200.0   150.0  570.0   150.0    70.0     NaN   \n",
       "健康科技      70.0   70.0     NaN    NaN     NaN    NaN    70.0   220.0    70.0   \n",
       "共享经济       NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN  2700.0   \n",
       "区块链        NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "即时通讯       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "大数据        NaN   70.0     NaN    NaN  1250.0    NaN     NaN     NaN   140.0   \n",
       "媒体和娱乐      NaN    NaN     NaN  270.0     NaN  200.0   570.0  1000.0     NaN   \n",
       "房地产科技      NaN    NaN   200.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "教育科技       NaN   70.0     NaN    NaN     NaN    NaN   100.0   140.0   470.0   \n",
       "新能源        NaN    NaN     NaN    NaN     NaN    NaN   140.0   140.0   140.0   \n",
       "新能源汽车      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新零售        NaN    NaN    70.0    NaN     NaN    NaN    70.0    70.0     NaN   \n",
       "机器人        NaN    NaN     NaN    NaN     NaN    NaN  1000.0     NaN     NaN   \n",
       "消费品        NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN     NaN   \n",
       "游戏         NaN    NaN     NaN    NaN   400.0    NaN     NaN   350.0   350.0   \n",
       "物流       100.0    NaN     NaN    NaN     NaN    NaN     NaN  1000.0   300.0   \n",
       "生命科学     150.0    NaN     NaN    NaN     NaN  150.0   150.0    70.0   800.0   \n",
       "电子商务       NaN    NaN     NaN    NaN   350.0  270.0   150.0   210.0   540.0   \n",
       "网络安全       NaN    NaN     NaN    NaN     NaN    NaN     NaN   570.0     NaN   \n",
       "航天         NaN    NaN  2500.0    NaN     NaN    NaN    70.0     NaN     NaN   \n",
       "虚拟与增强现实    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "软件与服务      NaN  100.0   150.0    NaN     NaN  100.0     NaN   210.0   100.0   \n",
       "金融科技     220.0    NaN   200.0    NaN     NaN  300.0   640.0   300.0   220.0   \n",
       "\n",
       "成立年份       2009    2010    2011    2012    2013     2014    2015    2016  \\\n",
       "行业                                                                         \n",
       "3D印刷        NaN     NaN    70.0     NaN   150.0      NaN   150.0     NaN   \n",
       "云计算       360.0   360.0   670.0   880.0   650.0    540.0   370.0     NaN   \n",
       "人工智能      320.0    70.0   440.0   360.0   440.0   1070.0   610.0  1310.0   \n",
       "健康科技      250.0   850.0   410.0   340.0   340.0      NaN   140.0   600.0   \n",
       "共享经济       70.0  3200.0   170.0  4600.0   420.0      NaN  1270.0   270.0   \n",
       "区块链         NaN     NaN    70.0   900.0  1300.0     70.0   150.0     NaN   \n",
       "即时通讯       70.0   150.0    70.0   220.0     NaN     70.0     NaN     NaN   \n",
       "大数据       100.0   140.0   210.0   370.0   200.0     70.0   170.0     NaN   \n",
       "媒体和娱乐     150.0     NaN  1350.0  5340.0   400.0    520.0   220.0     NaN   \n",
       "房地产科技       NaN   100.0   440.0   240.0    70.0      NaN   370.0     NaN   \n",
       "教育科技        NaN    70.0    70.0   270.0   270.0    340.0     NaN     NaN   \n",
       "新能源        70.0     NaN   220.0     NaN     NaN      NaN     NaN   150.0   \n",
       "新能源汽车     350.0     NaN     NaN     NaN     NaN   1070.0   520.0     NaN   \n",
       "新零售         NaN     NaN   220.0     NaN   220.0      NaN   140.0     NaN   \n",
       "机器人         NaN     NaN     NaN   300.0   100.0      NaN     NaN   200.0   \n",
       "消费品         NaN   150.0     NaN   370.0     NaN    140.0  3570.0    70.0   \n",
       "游戏          NaN     NaN     NaN   440.0     NaN      NaN   100.0     NaN   \n",
       "物流          NaN   370.0   740.0   770.0  2610.0   1180.0   360.0    70.0   \n",
       "生命科学      270.0   370.0    70.0   220.0    70.0    800.0   300.0   550.0   \n",
       "电子商务     1250.0  1750.0  1230.0  1900.0   170.0   1700.0   580.0   140.0   \n",
       "网络安全       70.0    70.0     NaN     NaN    70.0      NaN   200.0     NaN   \n",
       "航天          NaN     NaN     NaN   200.0     NaN      NaN     NaN     NaN   \n",
       "虚拟与增强现实     NaN   300.0   400.0    70.0     NaN      NaN     NaN     NaN   \n",
       "软件与服务     140.0   150.0    70.0   320.0   340.0    360.0     NaN     NaN   \n",
       "金融科技      210.0  2520.0  4040.0   990.0  2910.0  11570.0  1290.0   210.0   \n",
       "\n",
       "成立年份      2017   2018   2019  \n",
       "行业                            \n",
       "3D印刷       NaN    NaN    NaN  \n",
       "云计算        NaN    NaN    NaN  \n",
       "人工智能       NaN    NaN    NaN  \n",
       "健康科技     150.0   70.0  100.0  \n",
       "共享经济     370.0    NaN    NaN  \n",
       "区块链      300.0    NaN    NaN  \n",
       "即时通讯       NaN    NaN    NaN  \n",
       "大数据        NaN    NaN    NaN  \n",
       "媒体和娱乐      NaN    NaN    NaN  \n",
       "房地产科技      NaN  600.0    NaN  \n",
       "教育科技       NaN    NaN    NaN  \n",
       "新能源        NaN    NaN    NaN  \n",
       "新能源汽车    590.0    NaN    NaN  \n",
       "新零售       70.0   70.0    NaN  \n",
       "机器人        NaN    NaN    NaN  \n",
       "消费品      150.0  150.0    NaN  \n",
       "游戏         NaN    NaN    NaN  \n",
       "物流         NaN    NaN    NaN  \n",
       "生命科学       NaN    NaN    NaN  \n",
       "电子商务      70.0    NaN    NaN  \n",
       "网络安全       NaN    NaN   70.0  \n",
       "航天         NaN    NaN    NaN  \n",
       "虚拟与增强现实    NaN    NaN    NaN  \n",
       "软件与服务      NaN    NaN    NaN  \n",
       "金融科技     200.0  200.0    NaN  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_行业估值_年_pivot_table = df.reset_index().pivot_table(index=\"行业\",columns=\"成立年份\",values=\"估值（亿人民币）\",aggfunc=\"sum\")\n",
    "df_行业估值_年_pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available matplotlib backends: ['tk', 'gtk', 'gtk3', 'wx', 'qt4', 'qt5', 'qt', 'osx', 'nbagg', 'notebook', 'agg', 'svg', 'pdf', 'ps', 'inline', 'ipympl', 'widget']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20f7b162588>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21360 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21047 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21306 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22359 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 38142 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 25945 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32946 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 31185 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 25216 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 28040 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 36153 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21697 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32593 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32476 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23433 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20840 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21360 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21047 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21306 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22359 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 38142 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 25945 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32946 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 31185 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 25216 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 28040 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 36153 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21697 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32593 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32476 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23433 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20840 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 34892 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 19994 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 34892 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 19994 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 31435 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20221 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 31435 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20221 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib --list\n",
    "# D-3\n",
    "df_行业估值_年_pivot_table.plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实践groupby...agg...pivot方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv (\"20春_pandas_week02_hurun_unicorn.tsv\", encoding = \"utf8\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>金融科技</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>共享经济</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>云计算</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>消费品</td>\n",
       "      <td>3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>人工智能</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>教育科技</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>物流</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>494 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        行业  估值（亿人民币）\n",
       "0     金融科技     10000\n",
       "1    媒体和娱乐      5000\n",
       "2     共享经济      3600\n",
       "3      云计算      3500\n",
       "4      消费品      3400\n",
       "..     ...       ...\n",
       "489   人工智能        70\n",
       "490   教育科技        70\n",
       "491   电子商务        70\n",
       "492     物流        70\n",
       "493   电子商务        70\n",
       "\n",
       "[494 rows x 2 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_行业估值_年 = df[[\"行业\",\"估值（亿人民币）\"]]\n",
    "df_行业估值_年"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>成立年份</th>\n",
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       "      <th>行业</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>650.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.0</td>\n",
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       "      <td>440.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>410.0</td>\n",
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       "      <td>340.0</td>\n",
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       "      <td>100.0</td>\n",
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       "      <th>共享经济</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2700.0</td>\n",
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       "      <td>170.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>大数据</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>70.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>教育科技</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>590.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>新零售</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生命科学</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>140.0</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>网络安全</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>570.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>航天</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>虚拟与增强现实</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件与服务</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>4040.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>11570.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "成立年份      2000   2001    2002   2003    2004   2005    2006    2007    2008  \\\n",
       "行业                                                                            \n",
       "3D印刷       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "云计算      290.0   70.0  3500.0  150.0     NaN   70.0     NaN     NaN   290.0   \n",
       "人工智能       NaN    NaN     NaN  200.0   150.0  570.0   150.0    70.0     NaN   \n",
       "健康科技      70.0   70.0     NaN    NaN     NaN    NaN    70.0   220.0    70.0   \n",
       "共享经济       NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN  2700.0   \n",
       "区块链        NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "即时通讯       NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "大数据        NaN   70.0     NaN    NaN  1250.0    NaN     NaN     NaN   140.0   \n",
       "媒体和娱乐      NaN    NaN     NaN  270.0     NaN  200.0   570.0  1000.0     NaN   \n",
       "房地产科技      NaN    NaN   200.0    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "教育科技       NaN   70.0     NaN    NaN     NaN    NaN   100.0   140.0   470.0   \n",
       "新能源        NaN    NaN     NaN    NaN     NaN    NaN   140.0   140.0   140.0   \n",
       "新能源汽车      NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "新零售        NaN    NaN    70.0    NaN     NaN    NaN    70.0    70.0     NaN   \n",
       "机器人        NaN    NaN     NaN    NaN     NaN    NaN  1000.0     NaN     NaN   \n",
       "消费品        NaN    NaN     NaN    NaN     NaN    NaN   150.0     NaN     NaN   \n",
       "游戏         NaN    NaN     NaN    NaN   400.0    NaN     NaN   350.0   350.0   \n",
       "物流       100.0    NaN     NaN    NaN     NaN    NaN     NaN  1000.0   300.0   \n",
       "生命科学     150.0    NaN     NaN    NaN     NaN  150.0   150.0    70.0   800.0   \n",
       "电子商务       NaN    NaN     NaN    NaN   350.0  270.0   150.0   210.0   540.0   \n",
       "网络安全       NaN    NaN     NaN    NaN     NaN    NaN     NaN   570.0     NaN   \n",
       "航天         NaN    NaN  2500.0    NaN     NaN    NaN    70.0     NaN     NaN   \n",
       "虚拟与增强现实    NaN    NaN     NaN    NaN     NaN    NaN     NaN     NaN     NaN   \n",
       "软件与服务      NaN  100.0   150.0    NaN     NaN  100.0     NaN   210.0   100.0   \n",
       "金融科技     220.0    NaN   200.0    NaN     NaN  300.0   640.0   300.0   220.0   \n",
       "\n",
       "成立年份       2009    2010    2011    2012    2013     2014    2015    2016  \\\n",
       "行业                                                                         \n",
       "3D印刷        NaN     NaN    70.0     NaN   150.0      NaN   150.0     NaN   \n",
       "云计算       360.0   360.0   670.0   880.0   650.0    540.0   370.0     NaN   \n",
       "人工智能      320.0    70.0   440.0   360.0   440.0   1070.0   610.0  1310.0   \n",
       "健康科技      250.0   850.0   410.0   340.0   340.0      NaN   140.0   600.0   \n",
       "共享经济       70.0  3200.0   170.0  4600.0   420.0      NaN  1270.0   270.0   \n",
       "区块链         NaN     NaN    70.0   900.0  1300.0     70.0   150.0     NaN   \n",
       "即时通讯       70.0   150.0    70.0   220.0     NaN     70.0     NaN     NaN   \n",
       "大数据       100.0   140.0   210.0   370.0   200.0     70.0   170.0     NaN   \n",
       "媒体和娱乐     150.0     NaN  1350.0  5340.0   400.0    520.0   220.0     NaN   \n",
       "房地产科技       NaN   100.0   440.0   240.0    70.0      NaN   370.0     NaN   \n",
       "教育科技        NaN    70.0    70.0   270.0   270.0    340.0     NaN     NaN   \n",
       "新能源        70.0     NaN   220.0     NaN     NaN      NaN     NaN   150.0   \n",
       "新能源汽车     350.0     NaN     NaN     NaN     NaN   1070.0   520.0     NaN   \n",
       "新零售         NaN     NaN   220.0     NaN   220.0      NaN   140.0     NaN   \n",
       "机器人         NaN     NaN     NaN   300.0   100.0      NaN     NaN   200.0   \n",
       "消费品         NaN   150.0     NaN   370.0     NaN    140.0  3570.0    70.0   \n",
       "游戏          NaN     NaN     NaN   440.0     NaN      NaN   100.0     NaN   \n",
       "物流          NaN   370.0   740.0   770.0  2610.0   1180.0   360.0    70.0   \n",
       "生命科学      270.0   370.0    70.0   220.0    70.0    800.0   300.0   550.0   \n",
       "电子商务     1250.0  1750.0  1230.0  1900.0   170.0   1700.0   580.0   140.0   \n",
       "网络安全       70.0    70.0     NaN     NaN    70.0      NaN   200.0     NaN   \n",
       "航天          NaN     NaN     NaN   200.0     NaN      NaN     NaN     NaN   \n",
       "虚拟与增强现实     NaN   300.0   400.0    70.0     NaN      NaN     NaN     NaN   \n",
       "软件与服务     140.0   150.0    70.0   320.0   340.0    360.0     NaN     NaN   \n",
       "金融科技      210.0  2520.0  4040.0   990.0  2910.0  11570.0  1290.0   210.0   \n",
       "\n",
       "成立年份      2017   2018   2019  \n",
       "行业                            \n",
       "3D印刷       NaN    NaN    NaN  \n",
       "云计算        NaN    NaN    NaN  \n",
       "人工智能       NaN    NaN    NaN  \n",
       "健康科技     150.0   70.0  100.0  \n",
       "共享经济     370.0    NaN    NaN  \n",
       "区块链      300.0    NaN    NaN  \n",
       "即时通讯       NaN    NaN    NaN  \n",
       "大数据        NaN    NaN    NaN  \n",
       "媒体和娱乐      NaN    NaN    NaN  \n",
       "房地产科技      NaN  600.0    NaN  \n",
       "教育科技       NaN    NaN    NaN  \n",
       "新能源        NaN    NaN    NaN  \n",
       "新能源汽车    590.0    NaN    NaN  \n",
       "新零售       70.0   70.0    NaN  \n",
       "机器人        NaN    NaN    NaN  \n",
       "消费品      150.0  150.0    NaN  \n",
       "游戏         NaN    NaN    NaN  \n",
       "物流         NaN    NaN    NaN  \n",
       "生命科学       NaN    NaN    NaN  \n",
       "电子商务      70.0    NaN    NaN  \n",
       "网络安全       NaN    NaN   70.0  \n",
       "航天         NaN    NaN    NaN  \n",
       "虚拟与增强现实    NaN    NaN    NaN  \n",
       "软件与服务      NaN    NaN    NaN  \n",
       "金融科技     200.0  200.0    NaN  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_行业估值_年 = df.groupby(by = [\"行业\",\"成立年份\"])\\\n",
    "                .agg({\"估值（亿人民币）\":\"sum\"})\\\n",
    "                .reset_index()\\\n",
    "                .set_index([\"行业\"])\\\n",
    "                .pivot(columns=\"成立年份\",values=\"估值（亿人民币）\")\n",
    "\n",
    "df_行业估值_年"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available matplotlib backends: ['tk', 'gtk', 'gtk3', 'wx', 'qt4', 'qt5', 'qt', 'osx', 'nbagg', 'notebook', 'agg', 'svg', 'pdf', 'ps', 'inline', 'ipympl', 'widget']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20f7b5e5f08>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21360 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21047 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21306 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 22359 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 38142 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 25945 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32946 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 31185 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 25216 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 28040 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 36153 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 21697 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32593 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 32476 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 23433 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20840 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21360 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21047 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21306 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 22359 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 38142 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 25945 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32946 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 31185 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 25216 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 28040 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 36153 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 21697 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32593 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 32476 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 23433 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20840 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 34892 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 19994 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 34892 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 19994 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 31435 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 20221 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 31435 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 20221 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib --list\n",
    "# D-3\n",
    "df_行业估值_年.plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 小结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 本周内容通过对独角兽数据进行重塑，实现了对数据透视表的揉捏，并对比了pivot和pivot_table的使用  \n",
    "\n",
    "## 数据表是什么？\n",
    "- 透视表是一种交互表，能够动态改变排版，提供了揉捏数据得到有意义的结果更简便的方式。使用数据表需要我们对数据有充分的了解。在pandas中，pivot_table提供实现数据透视表的功能。  \n",
    "\n",
    "## pivot_table和groupby对比\n",
    "- pivot_table≈groupby...agg...pivot，比groupby更加灵活便捷易懂。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "256px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
